def prepare_expression(counts_df, tpm_df, vcf_lookup_s, sample_frac_threshold=0.2, count_threshold=6, tpm_threshold=0.1, mode='tmm'):
"""
Genes are thresholded based on the following expression rules:
TPM > tpm_threshold in >= sample_frac_threshold*samples
read counts >= count_threshold in sample_frac_threshold*samples
vcf_lookup: lookup table mapping sample IDs to VCF IDs
Between-sample normalization modes:
tmm: TMM from edgeR
qn: quantile normalization
"""
ix = np.intersect1d(counts_df.columns, vcf_lookup_s.index)
tpm_df = tpm_df[ix]
counts_df = counts_df[ix]
ns = tpm_df.shape[1]
# expression thresholds
mask = (
(np.sum(tpm_df>=tpm_threshold,axis=1)>=sample_frac_threshold*ns) &
(np.sum(counts_df>=count_threshold,axis=1)>=sample_frac_threshold*ns)
).values
# apply normalization
if mode.lower()=='tmm':
tmm_counts_df = rnaseqnorm.edgeR_cpm(counts_df, normalized_lib_sizes=True)
norm_df = rnaseqnorm.inverse_normal_transform(tmm_counts_df[mask])
elif mode.lower()=='qn':
qn_df = rnaseqnorm.normalize_quantiles(tpm_df.loc[mask])
norm_df = rnaseqnorm.inverse_normal_transform(qn_df)
else:
raise ValueError('Unsupported mode {}'.format(mode))
return norm_df
def prepare_expression(counts_df, tpm_df, mode='tmm'):
"""
This part and Normalization part is adapted from gtex official pipeline
Genes are thresholded based on the following expression rules:
TPM >= tpm_threshold in >= sample_frac_threshold*samples
read counts >= count_threshold in sample_frac_threshold*samples
vcf_lookup: lookup table mapping sample IDs to VCF IDs
Between-sample normalization modes:
tmm: TMM from edgeR
qn: quantile normalization
"""
ns = tpm_df.shape[1]
# apply normalization
if mode.lower() == 'tmm':
tmm_counts_df = rnaseqnorm.edgeR_cpm(counts_df,
normalized_lib_sizes=True)
norm_df = rnaseqnorm.inverse_normal_transform(tmm_counts_df)
elif mode.lower() == 'qn':
qn_df = rnaseqnorm.normalize_quantiles(tpm_df)
norm_df = rnaseqnorm.inverse_normal_transform(qn_df)
else:
raise ValueError('Unsupported mode {}'.format(mode))
return norm_df
def normalize_expression_and_generate_expression_pcs(
raw_pseudobulk_expression, sample_level_normalization,
gene_level_normalization, num_pcs, pb_expression_output_root):
# Initialize output normalized expression matrix
normalized_expression = np.zeros(raw_pseudobulk_expression.shape)
##################################
# Perform sample level normalization
##################################
if sample_level_normalization == 'qn':
df = pd.DataFrame(np.transpose(raw_pseudobulk_expression))
temp_out = rnaseqnorm.normalize_quantiles(df)
raw_pseudobulk_expression = np.transpose(np.asarray(temp_out))
##################################
# Perform gene level normalization
##################################
if gene_level_normalization == 'zscore':
for gene_num in range(normalized_expression.shape[1]):
temp_expr = (raw_pseudobulk_expression[:, gene_num] - np.mean(
raw_pseudobulk_expression[:, gene_num])) / np.std(
raw_pseudobulk_expression[:, gene_num])
temp_expr[temp_expr > 10.0] = 10.0
temp_expr[temp_expr < -10.0] = -10.0
temp_expr = temp_expr - np.mean(temp_expr)
normalized_expression[:, gene_num] = temp_expr
elif gene_level_normalization == 'ign':
# Code from GTEx v8
# Project each gene onto a gaussian
df = pd.DataFrame(np.transpose(raw_pseudobulk_expression))
norm_df = rnaseqnorm.inverse_normal_transform(df)
normalized_expression = np.transpose(np.asarray(norm_df))
else:
print(gene_level_normalization +
' gene level normalization method currently not implemented')
pdb.set_trace()
# Save normalized pseudobulk gene expression to output file
pseudobulk_expression_file = pb_expression_output_root + 'normalized_expression.txt'
np.savetxt(pseudobulk_expression_file,
normalized_expression,
fmt="%s",
delimiter='\t')
# Run PCA on pseudobulk data
pca_file = pb_expression_output_root + 'pca_scores.txt'
pca_ve_file = pb_expression_output_root + 'pca_pve.txt'
generate_pca_scores_and_variance_explained(pseudobulk_expression_file,
num_pcs, pca_file, pca_ve_file)
def standardize_expression(tpm_expression_matrix_file,
standardized_tpm_expression_matrix_file):
tpm_full = np.loadtxt(tpm_expression_matrix_file,
dtype=str,
delimiter='\t')
tpm = tpm_full[1:, 1:].astype(float)
samples = tpm_full[1:, 0]
genes = tpm_full[0, 1:]
# Quantile normalize the samples
df = pd.DataFrame(np.transpose(tpm))
#rank_mean = df.stack().groupby(df.rank(method='first').stack().astype(int)).mean()
#temp_out = df.rank(method='min').stack().astype(int).map(rank_mean).unstack()
#tpm_quantile_normalized = np.transpose(np.asarray(temp_out))
temp_out = rnaseqnorm.normalize_quantiles(df)
norm_df = rnaseqnorm.inverse_normal_transform(temp_out)
standardized_tpm = np.transpose(np.asarray(norm_df))
###
#tpm_quantile_normalized = np.transpose(np.asarray(temp_out))
###
# Standardize the genes
#num_genes = tpm_quantile_normalized.shape[1]
#num_samples = tpm_quantile_normalized.shape[0]
####
#standardized_tpm = np.zeros((num_samples, num_genes))
#for gene_num in range(num_genes):
# standardized_tpm[:,gene_num] = (tpm_quantile_normalized[:, gene_num] - np.mean(tpm_quantile_normalized[:, gene_num]))/np.std(tpm_quantile_normalized[:, gene_num])
####
# Print to output file
t = open(standardized_tpm_expression_matrix_file, 'w')
# print header
t.write('SampleId\t' + '\t'.join(samples) + '\n')
for gene_num, gene_name in enumerate(genes):
#expr = tpm_quantile_normalized[sample_num, :].astype(str)
###
expr = standardized_tpm[:, gene_num].astype(str)
###
t.write(gene_name + '\t' + '\t'.join(expr) + '\n')
t.close()
'''
def qn_normalization(tpm_df, mask):
qn_df = rnaseqnorm.normalize_quantiles(tpm_df.loc[mask])
norm_df = rnaseqnorm.inverse_normal_transform(qn_df)
return norm_df
def tmm_normalization(counts_df, mask):
tmm_counts_df = rnaseqnorm.edgeR_cpm(counts_df, normalized_lib_sizes=True)
norm_df = rnaseqnorm.inverse_normal_transform(tmm_counts_df[mask])
return norm_df
